code: def test_forward_with_small_bsz(
      self,
      batch_size,
      small_bsz,
      hidden_size,
      seq_len,
      heads,
      num_layers,
      is_preln,
      use_fp16,
  ):
      # Only run fp16 test cases on devices with FP16 capability.
      if not get_accelerator().is_fp16_supported() and use_fp16 is True:
          return
  
      ds_config = DeepSpeedTransformerConfig()
      ds_config.layer_id = None
      ds_config.batch_size = batch_size
      ds_config.hidden_size = hidden_size
      ds_config.intermediate_size = 4 * hidden_size
      ds_config.heads = heads
      ds_config.attn_dropout_ratio = 0.0
      ds_config.hidden_dropout_ratio = 0.0
      ds_config.num_hidden_layers = num_layers
      ds_config.pre_layer_norm = is_preln
      ds_config.initializer_range = 0.02
      ds_config.fp16 = use_fp16
  
      run_forward(ds_config, seq_len, atol=3e-2, test_bsz=small_bsz)
  
imports: import numpy
  import torch
  import pytest
  import random
  import copy
  from torch import nn
  from unit.modelingpreln import BertEncoder
  from unit.modeling import BertLayerNorm, BertConfig, BertEncoder
  from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
  from deepspeed.accelerator import get_accelerator
  from unit.common import DistributedTest
label_desc: Use image node_id 1 for calling the TestCUDAForwardSmallBatchSize obj's underlying member method code with example usage: obj.test_forward_with_small_bsz(batch_size, small_bsz, hidden_size, seq_len, heads, num_layers, is_preln, use_fp16) without return types